Artificial Intelligence Review

, Volume 43, Issue 4, pp 515–563 | Cite as

One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

  • Nauman Shahid
  • Ijaz Haider Naqvi
  • Saad Bin Qaisar
Article

Abstract

Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

Keywords

Wireless sensor networks Harsh environments Outlier detection  Event detection 

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Nauman Shahid
    • 1
  • Ijaz Haider Naqvi
    • 1
  • Saad Bin Qaisar
    • 2
  1. 1.Department of Electrical Engineering, School of Science and EngineeringLahore University of Management SciencesLahore CanttPakistan
  2. 2.Bitsym ResearchBitsym LLCBitsymUSA

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